For Startups
A senior engineer embedded with your team - not a consultant who delivers a document and disappears.
Start wherever makes sense. Many engagements begin with a Strategy Sprint and move into Fractional Engineering.
"Be the AI engineer we don't have."
A senior AI engineer embedded with your team. Strategy, hands-on engineering, evaluation infrastructure, and continuous improvement at a flexible monthly cadence.
Monthly cadence - flexible commitment Learn more"Show me where I stand and what to do next."
A clear technical roadmap: what to build, what to avoid, what to prioritise, and how you'll know it's working. The architectural decisions made before you write code.
Fixed price - 2-3 weeks Learn more"Is what we've built working?"
An honest, independent assessment of your existing AI system. What the evaluation infrastructure looks like, where the gaps are, and a clear plan for what to do about it.
Fixed price - 1-2 weeks Learn moreNot a retainer where you send questions and wait for answers.
Most AI projects fail not because the technology doesn't work, but because nobody is accountable for making it work in your context. Wrong architecture. Unstructured knowledge. No evaluation infrastructure. By the time that's obvious, rebuilding costs twice as much.
Fractional means a senior engineer there from architecture to production. Making decisions, writing code, building evaluation infrastructure - and staying long enough to know when something starts to break.
"The senior AI engineer your roadmap needs, without a full-time hire."Flexible monthly cadence - no lock-in
Every engagement starts the same way: define what good looks like before writing any code. Then build the measurement alongside the product.
Before any code is written, we define what the AI must do, what it must never do, and how you'll know the difference. This becomes the foundation - and the document you show investors, regulators, or your board.
Knowledge architecture, retrieval, and evaluation are designed together - not bolted on after launch. The result is a system that performs in production and gets better instead of degrading.
Continuous evaluation, drift detection, and impact assessment for model updates. You see degradation before your customers do - and production data becomes a flywheel for improvement.
Production AI built where wrong answers had real consequences.
A leading UK insurer needed to build their first customer-facing AI agent to regulatory standard. Off-the-shelf RAG wasn't meeting accuracy requirements. Daniel led a team of three, designed a GraphRAG architecture with a custom ontology, built evaluation infrastructure from day one, and took the system from concept to production.
Read the case studyA fraud detection startup needed consistent, deterministic AI their clients could depend on. Daniel designed a modular architecture with structured outputs and evaluation frameworks that made reliability measurable rather than assumed.
Read the case studyEnergy infrastructure professionals were drowning in thousands of pages of complex regulatory documentation. Co-founded the technical side: built a GraphRAG retrieval system and custom OCR pipeline from scratch. Concept to paying customers.
Read the case studyBook a free 30-minute call. Honest advice on where you are - no pitch, no obligation.
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